docs(design): ACE × codedb integration spec — design only, no impl#486
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Design draft sketching how codedb_context's ranking could benefit from a per-project Skillbook (boost/penalty path globs + keyword synonyms) learned by an external loop, without absorbing ACE's reflection machinery into codedb itself. Headline shape: - codedb owns deterministic, sub-ms read/write of a per-project skillbook.json - ACE (or any other learner) owns trace reflection + skill synthesis - Interface: `codedb_skillbook_update` MCP tool Three skill kinds for v0: path_boost, path_penalty, keyword_synonym. The doc commits to nothing yet — it preserves the option and gives future implementers/rejectors a concrete shape to work against rather than re-arguing "what if learning." Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Benchmark Regression ReportThresholds: 10.00% and 50,000 ns absolute delta
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… security Bumps semver to 0.2.5817. Bundles the v0.2.5816 perf+security release (PRs #484, #485, #483, #486, #487) with the experiment/reader-md feature that auto-prepends a hash-verified codebase map to codedb_context. Highlights vs v0.2.5815: Performance (PR #485, deterministic microbenchmarks): Suspense regex p50: 2.82 ms → 0.18 ms (15.6× faster) useState regex p99: 16.57 ms → 2.04 ms (8.1× p99 reduction) CLI surface (PR #484): + codedb read <path> [-L FROM-TO] [--compact] + path-safety + sensitive-file guards + project-root anchoring (uses configured root, not cwd) codedb_context (NEW in 0.2.5817): + auto-prepends .codedb/reader.md when source_hash matches + inline ~6 lines of body for ≤3 symbol_definitions + new "## Callers" section pre-surfaces execution sites + skip-on-short-task gate (≤80 chars) to avoid overhead on narrow lookups reader.md security (this branch): + path-traversal blocked (no absolute / .. in source_files) + source_files capped at 20 (DoS guard) + loc_actual capped at 240 (body bloat guard) + golden blake2b roundtrip test Eval (Sonnet 4.6, n=3 per task, vs v0.2.5815 main lineage): T1 flask median: 5 → 4 (-1) T2 regex median: 13 → 7 (-6) T3 react median: 13 → 10 (-3) All 9 runs across the matrix returned correct answers. Branch wins on median, mode, and best-case for every task. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
Adds `docs/design/ace-integration.md` — a design draft for how codedb_context could grow a per-project Skillbook (path boost/penalty globs + keyword synonyms) learned by an external system like kayba-ai/agentic-context-engine, without codedb itself absorbing trace reflection or LLM clients.
Why
The question came up: should codedb compete with ACE? Answer: no — they're different categories (ACE is agent memory, codedb is code search). But there's a real opportunity to let codedb consume learned skills as a deterministic read/write surface, while ACE owns the slow reflection loop.
Filing the spec so the option stays open, future implementers have a concrete starting shape, and future rejectors can point at this specific proposal instead of re-arguing "what if learning."
Shape
Estimated v0 implementation cost: 4-6 engineering days. This PR commits to none of it.
What this PR does NOT do
Test plan
🤖 Generated with Claude Code